Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations2500
Missing cells2494
Missing cells (%)5.0%
Duplicate rows3
Duplicate rows (%)0.1%
Total size in memory436.5 KiB
Average record size in memory178.8 B

Variable types

Categorical9
Numeric9
DateTime1
Unsupported1

Alerts

Dataset has 3 (0.1%) duplicate rowsDuplicates
Project Name has a high cardinality: 935 distinct valuesHigh cardinality
Street Name has a high cardinality: 687 distinct valuesHigh cardinality
Tenure has a high cardinality: 74 distinct valuesHigh cardinality
Area (SQFT) is highly overall correlated with Area (SQM) and 3 other fieldsHigh correlation
Area (SQM) is highly overall correlated with Area (SQFT) and 3 other fieldsHigh correlation
Floor Level is highly overall correlated with Type of AreaHigh correlation
Market Segment is highly overall correlated with Nett Price($) and 1 other fieldsHigh correlation
Nett Price($) is highly overall correlated with Area (SQFT) and 13 other fieldsHigh correlation
Number of Units is highly overall correlated with Nett Price($)High correlation
Postal District is highly overall correlated with Market Segment and 1 other fieldsHigh correlation
Property Type is highly overall correlated with Nett Price($) and 1 other fieldsHigh correlation
Sale Month is highly overall correlated with Nett Price($)High correlation
Sale Year is highly overall correlated with Nett Price($)High correlation
Tenure is highly overall correlated with Nett Price($) and 1 other fieldsHigh correlation
Transacted Price ($) is highly overall correlated with Area (SQFT) and 2 other fieldsHigh correlation
Type of Area is highly overall correlated with Area (SQFT) and 4 other fieldsHigh correlation
Type of Sale is highly overall correlated with Nett Price($) and 1 other fieldsHigh correlation
Unit Price ($ PSF) is highly overall correlated with Nett Price($) and 1 other fieldsHigh correlation
Unit Price ($ PSM) is highly overall correlated with Nett Price($) and 1 other fieldsHigh correlation
Type of Area is highly imbalanced (58.7%)Imbalance
Number of Units is highly imbalanced (99.2%)Imbalance
Nett Price($) has 2494 (99.8%) missing valuesMissing
Sale Quarter is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-10-05 11:59:02.292728
Analysis finished2025-10-05 11:59:07.902713
Duration5.61 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Project Name
Categorical

High cardinality 

Distinct935
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Memory size112.6 KiB
LANDED HOUSING DEVELOPMENT
 
77
NORMANTON PARK
 
30
CLAVON
 
19
TENET
 
18
PARC CLEMATIS
 
18
Other values (930)
2338 

Length

Max length47
Median length27
Mean length14.7216
Min length3

Characters and Unicode

Total characters36804
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique463 ?
Unique (%)18.5%

Sample

1st rowBARTLEY VUE
2nd rowAALTO
3rd rowRIVERGATE
4th rowMIDTOWN MODERN
5th rowDUO RESIDENCES

Common Values

ValueCountFrequency (%)
LANDED HOUSING DEVELOPMENT77
 
3.1%
NORMANTON PARK30
 
1.2%
CLAVON19
 
0.8%
TENET18
 
0.7%
PARC CLEMATIS18
 
0.7%
THE REEF AT KING'S DOCK16
 
0.6%
THE FLORENCE RESIDENCES16
 
0.6%
LENTOR HILLS RESIDENCES15
 
0.6%
GRAND DUNMAN15
 
0.6%
EMERALD OF KATONG15
 
0.6%
Other values (925)2261
90.4%

Length

2025-10-05T19:59:07.955394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the436
 
7.8%
residences322
 
5.7%
park198
 
3.5%
at145
 
2.6%
residence104
 
1.9%
parc81
 
1.4%
housing78
 
1.4%
grand78
 
1.4%
landed77
 
1.4%
development77
 
1.4%
Other values (928)4016
71.6%

Most occurring characters

ValueCountFrequency (%)
E4944
13.4%
3112
 
8.5%
A2931
 
8.0%
N2800
 
7.6%
R2771
 
7.5%
S2335
 
6.3%
I2271
 
6.2%
T2134
 
5.8%
O1921
 
5.2%
L1642
 
4.5%
Other values (31)9943
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)36804
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E4944
13.4%
3112
 
8.5%
A2931
 
8.0%
N2800
 
7.6%
R2771
 
7.5%
S2335
 
6.3%
I2271
 
6.2%
T2134
 
5.8%
O1921
 
5.2%
L1642
 
4.5%
Other values (31)9943
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36804
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E4944
13.4%
3112
 
8.5%
A2931
 
8.0%
N2800
 
7.6%
R2771
 
7.5%
S2335
 
6.3%
I2271
 
6.2%
T2134
 
5.8%
O1921
 
5.2%
L1642
 
4.5%
Other values (31)9943
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36804
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E4944
13.4%
3112
 
8.5%
A2931
 
8.0%
N2800
 
7.6%
R2771
 
7.5%
S2335
 
6.3%
I2271
 
6.2%
T2134
 
5.8%
O1921
 
5.2%
L1642
 
4.5%
Other values (31)9943
27.0%

Transacted Price ($)
Real number (ℝ)

High correlation 

Distinct1426
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2229047.7
Minimum470000
Maximum62000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 KiB
2025-10-05T19:59:08.022010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum470000
5-th percentile820717.25
Q11250000
median1660000
Q32386575
95-th percentile5163850
Maximum62000000
Range61530000
Interquartile range (IQR)1136575

Descriptive statistics

Standard deviation2518224.4
Coefficient of variation (CV)1.1297311
Kurtosis169.44035
Mean2229047.7
Median Absolute Deviation (MAD)500000
Skewness10.038511
Sum5.5726191 × 109
Variance6.3414543 × 1012
MonotonicityNot monotonic
2025-10-05T19:59:08.091330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000018
 
0.7%
135000017
 
0.7%
150000016
 
0.6%
180000016
 
0.6%
170000015
 
0.6%
160000014
 
0.6%
118000014
 
0.6%
165000014
 
0.6%
125000013
 
0.5%
115000013
 
0.5%
Other values (1416)2350
94.0%
ValueCountFrequency (%)
4700001
< 0.1%
4850001
< 0.1%
4926331
< 0.1%
5100001
< 0.1%
5400001
< 0.1%
5520001
< 0.1%
5700001
< 0.1%
5750001
< 0.1%
5880001
< 0.1%
5920001
< 0.1%
ValueCountFrequency (%)
620000001
< 0.1%
373000001
< 0.1%
320000002
0.1%
235000001
< 0.1%
230000001
< 0.1%
228000001
< 0.1%
220000001
< 0.1%
215000001
< 0.1%
185000001
< 0.1%
180000001
< 0.1%

Area (SQFT)
Real number (ℝ)

High correlation 

Distinct404
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1292.7806
Minimum344.45
Maximum21693.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 KiB
2025-10-05T19:59:08.161549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum344.45
5-th percentile495.14
Q1731.95
median1044.11
Q31356.26
95-th percentile3001.5165
Maximum21693.77
Range21349.32
Interquartile range (IQR)624.31

Descriptive statistics

Standard deviation1242.4996
Coefficient of variation (CV)0.96110629
Kurtosis111.1206
Mean1292.7806
Median Absolute Deviation (MAD)312.16
Skewness8.384841
Sum3231951.6
Variance1543805.3
MonotonicityNot monotonic
2025-10-05T19:59:08.229415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
678.1342
 
1.7%
936.4736
 
1.4%
731.9536
 
1.4%
882.6535
 
1.4%
656.634
 
1.4%
688.934
 
1.4%
1108.6932
 
1.3%
699.6632
 
1.3%
710.4231
 
1.2%
904.1830
 
1.2%
Other values (394)2158
86.3%
ValueCountFrequency (%)
344.451
 
< 0.1%
365.981
 
< 0.1%
376.741
 
< 0.1%
387.52
 
0.1%
398.273
 
0.1%
409.035
 
0.2%
419.87
0.3%
430.5616
0.6%
441.328
0.3%
452.0917
0.7%
ValueCountFrequency (%)
21693.771
< 0.1%
21583.971
< 0.1%
21108.21
< 0.1%
18689.531
< 0.1%
15929.641
< 0.1%
11022.341
< 0.1%
9850.141
< 0.1%
9462.631
< 0.1%
9189.231
< 0.1%
8493.871
< 0.1%

Unit Price ($ PSF)
Real number (ℝ)

High correlation 

Distinct1504
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1778.2684
Minimum274
Maximum5397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 KiB
2025-10-05T19:59:08.296967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum274
5-th percentile901.95
Q11291.75
median1678.5
Q32195
95-th percentile2901.05
Maximum5397
Range5123
Interquartile range (IQR)903.25

Descriptive statistics

Standard deviation631.04486
Coefficient of variation (CV)0.3548648
Kurtosis0.81103657
Mean1778.2684
Median Absolute Deviation (MAD)434.5
Skewness0.73385087
Sum4445671
Variance398217.62
MonotonicityNot monotonic
2025-10-05T19:59:08.370845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15488
 
0.3%
12147
 
0.3%
12397
 
0.3%
14427
 
0.3%
13947
 
0.3%
17876
 
0.2%
9296
 
0.2%
16396
 
0.2%
15995
 
0.2%
12515
 
0.2%
Other values (1494)2436
97.4%
ValueCountFrequency (%)
2741
< 0.1%
3021
< 0.1%
4481
< 0.1%
5161
< 0.1%
5971
< 0.1%
6061
< 0.1%
6151
< 0.1%
6161
< 0.1%
6251
< 0.1%
6331
< 0.1%
ValueCountFrequency (%)
53971
< 0.1%
52351
< 0.1%
47481
< 0.1%
46451
< 0.1%
42641
< 0.1%
41691
< 0.1%
38821
< 0.1%
38331
< 0.1%
38031
< 0.1%
37451
< 0.1%
Distinct61
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size39.1 KiB
Minimum2020-09-01 00:00:00
Maximum2025-09-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-05T19:59:08.442688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-05T19:59:08.525842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Street Name
Categorical

High cardinality 

Distinct687
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size105.1 KiB
TAMPINES STREET 62
 
37
FLORA DRIVE
 
34
NORMANTON PARK
 
30
CANBERRA DRIVE
 
30
HILLVIEW RISE
 
29
Other values (682)
2340 

Length

Max length26
Median length22
Mean length14.926
Min length8

Characters and Unicode

Total characters37315
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique298 ?
Unique (%)11.9%

Sample

1st rowJALAN BUNGA RAMPAI
2nd rowMEYER ROAD
3rd rowROBERTSON QUAY
4th rowTAN QUEE LAN STREET
5th rowFRASER STREET

Common Values

ValueCountFrequency (%)
TAMPINES STREET 6237
 
1.5%
FLORA DRIVE34
 
1.4%
NORMANTON PARK30
 
1.2%
CANBERRA DRIVE30
 
1.2%
HILLVIEW RISE29
 
1.2%
CLEMENTI AVENUE 129
 
1.2%
LENTOR CENTRAL25
 
1.0%
TAMPINES STREET 8625
 
1.0%
JALAN LEMPENG24
 
1.0%
JALAN TEMBUSU23
 
0.9%
Other values (677)2214
88.6%

Length

2025-10-05T19:59:08.611211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
road684
 
10.7%
avenue308
 
4.8%
drive209
 
3.3%
street181
 
2.8%
jalan165
 
2.6%
tampines95
 
1.5%
lorong89
 
1.4%
upper88
 
1.4%
188
 
1.4%
walk79
 
1.2%
Other values (613)4378
68.8%

Most occurring characters

ValueCountFrequency (%)
A4071
 
10.9%
3864
 
10.4%
E3784
 
10.1%
R3060
 
8.2%
N2602
 
7.0%
O2483
 
6.7%
I1760
 
4.7%
L1744
 
4.7%
T1718
 
4.6%
S1598
 
4.3%
Other values (30)10631
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)37315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A4071
 
10.9%
3864
 
10.4%
E3784
 
10.1%
R3060
 
8.2%
N2602
 
7.0%
O2483
 
6.7%
I1760
 
4.7%
L1744
 
4.7%
T1718
 
4.6%
S1598
 
4.3%
Other values (30)10631
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)37315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A4071
 
10.9%
3864
 
10.4%
E3784
 
10.1%
R3060
 
8.2%
N2602
 
7.0%
O2483
 
6.7%
I1760
 
4.7%
L1744
 
4.7%
T1718
 
4.6%
S1598
 
4.3%
Other values (30)10631
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)37315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A4071
 
10.9%
3864
 
10.4%
E3784
 
10.1%
R3060
 
8.2%
N2602
 
7.0%
O2483
 
6.7%
I1760
 
4.7%
L1744
 
4.7%
T1718
 
4.6%
S1598
 
4.3%
Other values (30)10631
28.5%

Type of Sale
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.1 KiB
Resale
1508 
New Sale
908 
Sub Sale
 
84

Length

Max length8
Median length6
Mean length6.7936
Min length6

Characters and Unicode

Total characters16984
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew Sale
2nd rowResale
3rd rowResale
4th rowNew Sale
5th rowResale

Common Values

ValueCountFrequency (%)
Resale1508
60.3%
New Sale908
36.3%
Sub Sale84
 
3.4%

Length

2025-10-05T19:59:08.679922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-05T19:59:08.731993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
resale1508
43.2%
sale992
28.4%
new908
26.0%
sub84
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e4916
28.9%
a2500
14.7%
l2500
14.7%
R1508
 
8.9%
s1508
 
8.9%
S1076
 
6.3%
992
 
5.8%
N908
 
5.3%
w908
 
5.3%
u84
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)16984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e4916
28.9%
a2500
14.7%
l2500
14.7%
R1508
 
8.9%
s1508
 
8.9%
S1076
 
6.3%
992
 
5.8%
N908
 
5.3%
w908
 
5.3%
u84
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e4916
28.9%
a2500
14.7%
l2500
14.7%
R1508
 
8.9%
s1508
 
8.9%
S1076
 
6.3%
992
 
5.8%
N908
 
5.3%
w908
 
5.3%
u84
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e4916
28.9%
a2500
14.7%
l2500
14.7%
R1508
 
8.9%
s1508
 
8.9%
S1076
 
6.3%
992
 
5.8%
N908
 
5.3%
w908
 
5.3%
u84
 
0.5%

Type of Area
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.1 KiB
Strata
2292 
Land
 
208

Length

Max length6
Median length6
Mean length5.8336
Min length4

Characters and Unicode

Total characters14584
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStrata
2nd rowStrata
3rd rowStrata
4th rowStrata
5th rowStrata

Common Values

ValueCountFrequency (%)
Strata2292
91.7%
Land208
 
8.3%

Length

2025-10-05T19:59:08.784894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-05T19:59:08.823441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
strata2292
91.7%
land208
 
8.3%

Most occurring characters

ValueCountFrequency (%)
a4792
32.9%
t4584
31.4%
S2292
15.7%
r2292
15.7%
L208
 
1.4%
n208
 
1.4%
d208
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)14584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a4792
32.9%
t4584
31.4%
S2292
15.7%
r2292
15.7%
L208
 
1.4%
n208
 
1.4%
d208
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a4792
32.9%
t4584
31.4%
S2292
15.7%
r2292
15.7%
L208
 
1.4%
n208
 
1.4%
d208
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a4792
32.9%
t4584
31.4%
S2292
15.7%
r2292
15.7%
L208
 
1.4%
n208
 
1.4%
d208
 
1.4%

Area (SQM)
Real number (ℝ)

High correlation 

Distinct404
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.10226
Minimum32
Maximum2015.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 KiB
2025-10-05T19:59:08.876986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile46
Q168
median97
Q3126
95-th percentile278.848
Maximum2015.4
Range1983.4
Interquartile range (IQR)58

Descriptive statistics

Standard deviation115.43104
Coefficient of variation (CV)0.96110632
Kurtosis111.1206
Mean120.10226
Median Absolute Deviation (MAD)29
Skewness8.3848407
Sum300255.64
Variance13324.324
MonotonicityNot monotonic
2025-10-05T19:59:08.953326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6342
 
1.7%
8736
 
1.4%
6836
 
1.4%
8235
 
1.4%
6134
 
1.4%
6434
 
1.4%
10332
 
1.3%
6532
 
1.3%
6631
 
1.2%
8430
 
1.2%
Other values (394)2158
86.3%
ValueCountFrequency (%)
321
 
< 0.1%
341
 
< 0.1%
351
 
< 0.1%
362
 
0.1%
373
 
0.1%
385
 
0.2%
397
0.3%
4016
0.6%
418
0.3%
4217
0.7%
ValueCountFrequency (%)
2015.41
< 0.1%
2005.21
< 0.1%
19611
< 0.1%
1736.31
< 0.1%
1479.91
< 0.1%
10241
< 0.1%
915.11
< 0.1%
879.11
< 0.1%
853.71
< 0.1%
789.11
< 0.1%

Unit Price ($ PSM)
Real number (ℝ)

High correlation 

Distinct2279
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19141.22
Minimum2950
Maximum58099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 KiB
2025-10-05T19:59:09.229369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2950
5-th percentile9705.7
Q113899.5
median18066.5
Q323623
95-th percentile31226.75
Maximum58099
Range55149
Interquartile range (IQR)9723.5

Descriptive statistics

Standard deviation6792.5084
Coefficient of variation (CV)0.35486288
Kurtosis0.81144083
Mean19141.22
Median Absolute Deviation (MAD)4680.5
Skewness0.73393804
Sum47853050
Variance46138170
MonotonicityNot monotonic
2025-10-05T19:59:09.298655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150007
 
0.3%
133337
 
0.3%
166677
 
0.3%
100006
 
0.2%
180005
 
0.2%
125004
 
0.2%
200004
 
0.2%
170004
 
0.2%
250003
 
0.1%
215733
 
0.1%
Other values (2269)2450
98.0%
ValueCountFrequency (%)
29501
< 0.1%
32511
< 0.1%
48191
< 0.1%
55561
< 0.1%
64211
< 0.1%
65241
< 0.1%
66231
< 0.1%
66251
< 0.1%
67311
< 0.1%
68151
< 0.1%
ValueCountFrequency (%)
580991
< 0.1%
563551
< 0.1%
511051
< 0.1%
500001
< 0.1%
458961
< 0.1%
448721
< 0.1%
417891
< 0.1%
412621
< 0.1%
409351
< 0.1%
403131
< 0.1%

Nett Price($)
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)100.0%
Missing2494
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean1804327.2
Minimum826428
Maximum3445100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 KiB
2025-10-05T19:59:09.348549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum826428
5-th percentile842088.5
Q1960043.75
median1523182.5
Q32432600
95-th percentile3238575
Maximum3445100
Range2618672
Interquartile range (IQR)1472556.2

Descriptive statistics

Standard deviation1053869
Coefficient of variation (CV)0.58407865
Kurtosis-0.87980052
Mean1804327.2
Median Absolute Deviation (MAD)665433.5
Skewness0.77012027
Sum10825963
Variance1.1106398 × 1012
MonotonicityNot monotonic
2025-10-05T19:59:09.397414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8890701
 
< 0.1%
34451001
 
< 0.1%
26190001
 
< 0.1%
18734001
 
< 0.1%
8264281
 
< 0.1%
11729651
 
< 0.1%
(Missing)2494
99.8%
ValueCountFrequency (%)
8264281
< 0.1%
8890701
< 0.1%
11729651
< 0.1%
18734001
< 0.1%
26190001
< 0.1%
34451001
< 0.1%
ValueCountFrequency (%)
34451001
< 0.1%
26190001
< 0.1%
18734001
< 0.1%
11729651
< 0.1%
8890701
< 0.1%
8264281
< 0.1%

Property Type
Categorical

High correlation 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
Condominium
994 
Apartment
988 
Executive Condominium
281 
Terrace House
129 
Semi-Detached House
 
78

Length

Max length21
Median length19
Mean length11.7224
Min length9

Characters and Unicode

Total characters29306
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApartment
2nd rowCondominium
3rd rowApartment
4th rowApartment
5th rowApartment

Common Values

ValueCountFrequency (%)
Condominium994
39.8%
Apartment988
39.5%
Executive Condominium281
 
11.2%
Terrace House129
 
5.2%
Semi-Detached House78
 
3.1%
Detached House30
 
1.2%

Length

2025-10-05T19:59:09.452769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-05T19:59:09.499167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
condominium1275
42.2%
apartment988
32.7%
executive281
 
9.3%
house237
 
7.9%
terrace129
 
4.3%
semi-detached78
 
2.6%
detached30
 
1.0%

Most occurring characters

ValueCountFrequency (%)
m3616
12.3%
n3538
12.1%
i2909
9.9%
o2787
9.5%
t2365
 
8.1%
e2339
 
8.0%
u1793
 
6.1%
d1383
 
4.7%
C1275
 
4.4%
r1246
 
4.3%
Other values (15)6055
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)29306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m3616
12.3%
n3538
12.1%
i2909
9.9%
o2787
9.5%
t2365
 
8.1%
e2339
 
8.0%
u1793
 
6.1%
d1383
 
4.7%
C1275
 
4.4%
r1246
 
4.3%
Other values (15)6055
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)29306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m3616
12.3%
n3538
12.1%
i2909
9.9%
o2787
9.5%
t2365
 
8.1%
e2339
 
8.0%
u1793
 
6.1%
d1383
 
4.7%
C1275
 
4.4%
r1246
 
4.3%
Other values (15)6055
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)29306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m3616
12.3%
n3538
12.1%
i2909
9.9%
o2787
9.5%
t2365
 
8.1%
e2339
 
8.0%
u1793
 
6.1%
d1383
 
4.7%
C1275
 
4.4%
r1246
 
4.3%
Other values (15)6055
20.7%

Number of Units
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size39.1 KiB
1
2497 
3
 
1
2
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2500
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12497
99.9%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

Length

2025-10-05T19:59:09.563424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-05T19:59:09.600228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12497
99.9%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
12497
99.9%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12497
99.9%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12497
99.9%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12497
99.9%
31
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%

Tenure
Categorical

High cardinality  High correlation 

Distinct74
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size29.6 KiB
Freehold
724 
99 yrs lease commencing from 2018
176 
99 yrs lease commencing from 2019
128 
99 yrs lease commencing from 2021
 
116
99 yrs lease commencing from 2023
 
106
Other values (69)
1250 

Length

Max length34
Median length33
Mean length25.8048
Min length8

Characters and Unicode

Total characters64512
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.6%

Sample

1st row99 yrs lease commencing from 2020
2nd rowFreehold
3rd rowFreehold
4th row99 yrs lease commencing from 2019
5th row99 yrs lease commencing from 2011

Common Values

ValueCountFrequency (%)
Freehold724
29.0%
99 yrs lease commencing from 2018176
 
7.0%
99 yrs lease commencing from 2019128
 
5.1%
99 yrs lease commencing from 2021116
 
4.6%
99 yrs lease commencing from 2023106
 
4.2%
99 yrs lease commencing from 2022102
 
4.1%
99 yrs lease commencing from 2011100
 
4.0%
99 yrs lease commencing from 202499
 
4.0%
99 yrs lease commencing from 201282
 
3.3%
99 yrs lease commencing from 201379
 
3.2%
Other values (64)788
31.5%

Length

2025-10-05T19:59:09.650888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
from1776
15.6%
yrs1776
15.6%
lease1776
15.6%
commencing1776
15.6%
991660
14.6%
freehold724
6.4%
2018176
 
1.5%
2019128
 
1.1%
2021116
 
1.0%
2023106
 
0.9%
Other values (69)1366
12.0%

Most occurring characters

ValueCountFrequency (%)
8880
13.8%
e6776
 
10.5%
m5328
 
8.3%
o4276
 
6.6%
r4276
 
6.6%
94206
 
6.5%
n3552
 
5.5%
s3552
 
5.5%
c3552
 
5.5%
l2500
 
3.9%
Other values (17)17614
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)64512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8880
13.8%
e6776
 
10.5%
m5328
 
8.3%
o4276
 
6.6%
r4276
 
6.6%
94206
 
6.5%
n3552
 
5.5%
s3552
 
5.5%
c3552
 
5.5%
l2500
 
3.9%
Other values (17)17614
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)64512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8880
13.8%
e6776
 
10.5%
m5328
 
8.3%
o4276
 
6.6%
r4276
 
6.6%
94206
 
6.5%
n3552
 
5.5%
s3552
 
5.5%
c3552
 
5.5%
l2500
 
3.9%
Other values (17)17614
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)64512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8880
13.8%
e6776
 
10.5%
m5328
 
8.3%
o4276
 
6.6%
r4276
 
6.6%
94206
 
6.5%
n3552
 
5.5%
s3552
 
5.5%
c3552
 
5.5%
l2500
 
3.9%
Other values (17)17614
27.3%

Postal District
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.782
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 KiB
2025-10-05T19:59:09.705397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median16
Q321
95-th percentile27
Maximum28
Range27
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.9875608
Coefficient of variation (CV)0.44275509
Kurtosis-0.76318237
Mean15.782
Median Absolute Deviation (MAD)5
Skewness-0.26065853
Sum39455
Variance48.826006
MonotonicityNot monotonic
2025-10-05T19:59:09.766121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
19296
 
11.8%
15224
 
9.0%
23164
 
6.6%
18158
 
6.3%
5152
 
6.1%
10150
 
6.0%
21116
 
4.6%
16104
 
4.2%
996
 
3.8%
1495
 
3.8%
Other values (18)945
37.8%
ValueCountFrequency (%)
132
 
1.3%
218
 
0.7%
393
3.7%
444
 
1.8%
5152
6.1%
616
 
0.6%
720
 
0.8%
830
 
1.2%
996
3.8%
10150
6.0%
ValueCountFrequency (%)
2865
 
2.6%
2790
 
3.6%
2689
 
3.6%
2544
 
1.8%
2425
 
1.0%
23164
6.6%
2261
 
2.4%
21116
 
4.6%
2074
 
3.0%
19296
11.8%

Market Segment
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.1 KiB
Outside Central Region
1380 
Rest of Central Region
761 
Core Central Region
359 

Length

Max length22
Median length22
Mean length21.5692
Min length19

Characters and Unicode

Total characters53923
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRest of Central Region
2nd rowRest of Central Region
3rd rowCore Central Region
4th rowCore Central Region
5th rowCore Central Region

Common Values

ValueCountFrequency (%)
Outside Central Region1380
55.2%
Rest of Central Region761
30.4%
Core Central Region359
 
14.4%

Length

2025-10-05T19:59:09.827556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-05T19:59:09.876449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
central2500
30.3%
region2500
30.3%
outside1380
16.7%
rest761
 
9.2%
of761
 
9.2%
core359
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e7500
13.9%
5761
10.7%
n5000
9.3%
t4641
 
8.6%
i3880
 
7.2%
o3620
 
6.7%
R3261
 
6.0%
C2859
 
5.3%
r2859
 
5.3%
g2500
 
4.6%
Other values (7)12042
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)53923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e7500
13.9%
5761
10.7%
n5000
9.3%
t4641
 
8.6%
i3880
 
7.2%
o3620
 
6.7%
R3261
 
6.0%
C2859
 
5.3%
r2859
 
5.3%
g2500
 
4.6%
Other values (7)12042
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)53923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e7500
13.9%
5761
10.7%
n5000
9.3%
t4641
 
8.6%
i3880
 
7.2%
o3620
 
6.7%
R3261
 
6.0%
C2859
 
5.3%
r2859
 
5.3%
g2500
 
4.6%
Other values (7)12042
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)53923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e7500
13.9%
5761
10.7%
n5000
9.3%
t4641
 
8.6%
i3880
 
7.2%
o3620
 
6.7%
R3261
 
6.0%
C2859
 
5.3%
r2859
 
5.3%
g2500
 
4.6%
Other values (7)12042
22.3%

Floor Level
Categorical

High correlation 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.6 KiB
01 to 05
849 
06 to 10
561 
11 to 15
397 
-
239 
16 to 20
224 
Other values (9)
230 

Length

Max length8
Median length8
Mean length7.3308
Min length1

Characters and Unicode

Total characters18327
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row01 to 05
2nd row16 to 20
3rd row36 to 40
4th row11 to 15
5th row11 to 15

Common Values

ValueCountFrequency (%)
01 to 05849
34.0%
06 to 10561
22.4%
11 to 15397
15.9%
-239
 
9.6%
16 to 20224
 
9.0%
21 to 25109
 
4.4%
26 to 3048
 
1.9%
31 to 3533
 
1.3%
36 to 4018
 
0.7%
41 to 4511
 
0.4%
Other values (4)11
 
0.4%

Length

2025-10-05T19:59:09.930812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to2261
32.2%
01849
 
12.1%
05849
 
12.1%
06561
 
8.0%
10561
 
8.0%
11397
 
5.7%
15397
 
5.7%
239
 
3.4%
16224
 
3.2%
20224
 
3.2%
Other values (18)460
 
6.6%

Most occurring characters

ValueCountFrequency (%)
4522
24.7%
03115
17.0%
12984
16.3%
t2261
12.3%
o2261
12.3%
51418
 
7.7%
6861
 
4.7%
2490
 
2.7%
-239
 
1.3%
3132
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)18327
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4522
24.7%
03115
17.0%
12984
16.3%
t2261
12.3%
o2261
12.3%
51418
 
7.7%
6861
 
4.7%
2490
 
2.7%
-239
 
1.3%
3132
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18327
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4522
24.7%
03115
17.0%
12984
16.3%
t2261
12.3%
o2261
12.3%
51418
 
7.7%
6861
 
4.7%
2490
 
2.7%
-239
 
1.3%
3132
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18327
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4522
24.7%
03115
17.0%
12984
16.3%
t2261
12.3%
o2261
12.3%
51418
 
7.7%
6861
 
4.7%
2490
 
2.7%
-239
 
1.3%
3132
 
0.7%

Sale Year
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2022.548
Minimum2020
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2025-10-05T19:59:09.976721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2020
5-th percentile2020
Q12021
median2022
Q32024
95-th percentile2025
Maximum2025
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5421567
Coefficient of variation (CV)0.00076248213
Kurtosis-1.2163672
Mean2022.548
Median Absolute Deviation (MAD)1
Skewness0.14499739
Sum5056370
Variance2.3782473
MonotonicityNot monotonic
2025-10-05T19:59:10.025086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021680
27.2%
2024473
18.9%
2022454
18.2%
2023390
15.6%
2025344
13.8%
2020159
 
6.4%
ValueCountFrequency (%)
2020159
 
6.4%
2021680
27.2%
2022454
18.2%
2023390
15.6%
2024473
18.9%
2025344
13.8%
ValueCountFrequency (%)
2025344
13.8%
2024473
18.9%
2023390
15.6%
2022454
18.2%
2021680
27.2%
2020159
 
6.4%

Sale Month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6804
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2025-10-05T19:59:10.070279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3822525
Coefficient of variation (CV)0.5062949
Kurtosis-1.1796788
Mean6.6804
Median Absolute Deviation (MAD)3
Skewness-0.02814891
Sum16701
Variance11.439632
MonotonicityNot monotonic
2025-10-05T19:59:10.122966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11274
11.0%
7258
10.3%
4240
9.6%
5239
9.6%
3219
8.8%
8218
8.7%
12200
8.0%
10185
7.4%
9182
7.3%
1181
7.2%
Other values (2)304
12.2%
ValueCountFrequency (%)
1181
7.2%
2142
5.7%
3219
8.8%
4240
9.6%
5239
9.6%
6162
6.5%
7258
10.3%
8218
8.7%
9182
7.3%
10185
7.4%
ValueCountFrequency (%)
12200
8.0%
11274
11.0%
10185
7.4%
9182
7.3%
8218
8.7%
7258
10.3%
6162
6.5%
5239
9.6%
4240
9.6%
3219
8.8%

Sale Quarter
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size39.1 KiB

Interactions

2025-10-05T19:59:07.088742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-05T19:59:07.034787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-05T19:59:10.181412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Area (SQFT)Area (SQM)Floor LevelMarket SegmentNett Price($)Number of UnitsPostal DistrictProperty TypeSale MonthSale YearTenureTransacted Price ($)Type of AreaType of SaleUnit Price ($ PSF)Unit Price ($ PSM)
Area (SQFT)1.0001.0000.2570.1250.9430.2780.0450.4150.002-0.0650.0330.7000.5270.131-0.269-0.269
Area (SQM)1.0001.0000.2570.1250.9430.2780.0450.4150.002-0.0650.0330.7000.5270.131-0.269-0.269
Floor Level0.2570.2571.0000.1650.3540.0000.1780.4470.0250.0430.1890.3410.9240.1950.2430.243
Market Segment0.1250.1250.1651.0000.5000.0370.7980.2700.0540.0170.3550.1750.0780.0940.4240.424
Nett Price($)0.9430.9430.3540.5001.0001.000-0.7780.500-0.5800.9120.5001.0001.0001.0000.8860.886
Number of Units0.2780.2780.0000.0371.0001.0000.0250.0000.0000.0000.0000.1530.0000.0000.0000.000
Postal District0.0450.0450.1780.798-0.7780.0251.0000.2360.0270.0970.362-0.2430.1290.189-0.408-0.408
Property Type0.4150.4150.4470.2700.5000.0000.2361.0000.0470.0510.3330.2870.9300.2780.1800.179
Sale Month0.0020.0020.0250.054-0.5800.0000.0270.0471.000-0.1910.107-0.0120.0470.043-0.012-0.012
Sale Year-0.065-0.0650.0430.0170.9120.0000.0970.051-0.1911.0000.2800.1620.0550.0970.3040.304
Tenure0.0330.0330.1890.3550.5000.0000.3620.3330.1070.2801.0000.0000.4580.6270.2230.223
Transacted Price ($)0.7000.7000.3410.1751.0000.153-0.2430.287-0.0120.1620.0001.0000.3090.0320.4360.436
Type of Area0.5270.5270.9240.0781.0000.0000.1290.9300.0470.0550.4580.3091.0000.2220.0690.069
Type of Sale0.1310.1310.1950.0941.0000.0000.1890.2780.0430.0970.6270.0320.2221.0000.3790.379
Unit Price ($ PSF)-0.269-0.2690.2430.4240.8860.000-0.4080.180-0.0120.3040.2230.4360.0690.3791.0001.000
Unit Price ($ PSM)-0.269-0.2690.2430.4240.8860.000-0.4080.179-0.0120.3040.2230.4360.0690.3791.0001.000

Missing values

2025-10-05T19:59:07.722079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-05T19:59:07.824792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Project NameTransacted Price ($)Area (SQFT)Unit Price ($ PSF)Sale DateStreet NameType of SaleType of AreaArea (SQM)Unit Price ($ PSM)Nett Price($)Property TypeNumber of UnitsTenurePostal DistrictMarket SegmentFloor LevelSale YearSale MonthSale Quarter
54732BARTLEY VUE21480001,044.1120572024-11-01JALAN BUNGA RAMPAINew SaleStrata97.0022144NaNApartment199 yrs lease commencing from 202019Rest of Central Region01 to 052024112024Q4
27986AALTO52000001,959.0526542024-08-01MEYER ROADResaleStrata182.0028571NaNCondominium1Freehold15Rest of Central Region16 to 20202482024Q3
129862RIVERGATE48000001,550.0230972022-11-01ROBERTSON QUAYResaleStrata144.0033333NaNApartment1Freehold9Core Central Region36 to 402022112022Q4
137538MIDTOWN MODERN1292000409.0331592021-03-01TAN QUEE LAN STREETNew SaleStrata38.0034000NaNApartment199 yrs lease commencing from 20197Core Central Region11 to 15202132021Q1
134985DUO RESIDENCES1880000818.0622982021-10-01FRASER STREETResaleStrata76.0024737NaNApartment199 yrs lease commencing from 20117Core Central Region11 to 152021102021Q4
45164AMBER PARK37138001,582.3123472020-11-01AMBER GARDENSNew SaleStrata147.0025264NaNCondominium1Freehold15Rest of Central Region01 to 052020112020Q4
109792HILLINGTON GREEN18000001,528.4911782021-02-01HILLVIEW AVENUEResaleStrata142.0012676NaNCondominium1999 yrs lease commencing from 188323Outside Central Region01 to 05202122021Q1
120676PROVENCE RESIDENCE12470001,065.6411702021-05-01CANBERRA CRESCENTNew SaleStrata99.0012596NaNExecutive Condominium199 yrs lease commencing from 202027Outside Central Region11 to 15202152021Q2
33358THE CONTINUUM1688000645.8426142023-05-01THIAM SIEW AVENUENew SaleStrata60.0028133NaNApartment1Freehold15Rest of Central Region01 to 05202352023Q2
85456WESTVILLE26501682,152.8012312024-10-01WESTWOOD WALKResaleLand200.0013251NaNSemi-Detached House199 yrs lease commencing from 199422Outside Central Region-2024102024Q4
Project NameTransacted Price ($)Area (SQFT)Unit Price ($ PSF)Sale DateStreet NameType of SaleType of AreaArea (SQM)Unit Price ($ PSM)Nett Price($)Property TypeNumber of UnitsTenurePostal DistrictMarket SegmentFloor LevelSale YearSale MonthSale Quarter
96335LENTOR MANSION1268000527.4424042024-03-01LENTOR GARDENSNew SaleStrata49.0025878NaNApartment199 yrs lease commencing from 202326Outside Central Region11 to 15202432024Q1
32565VIIO @ BALESTIER18050001,076.4016772023-07-01BALESTIER ROADResaleStrata100.0018050NaNApartment1Freehold12Rest of Central Region06 to 10202372023Q3
25486AMBER PARK33000001,108.6929762025-01-01AMBER GARDENSResaleStrata103.0032039NaNCondominium1Freehold15Rest of Central Region06 to 10202512025Q1
31799SUITES @ GUILLEMARD775000462.8516742023-08-01LIM AH WOO ROADResaleStrata43.0018023NaNApartment1Freehold15Rest of Central Region01 to 05202382023Q3
109874SIGNATURE PARK13100001,033.3412682021-02-01TOH TUCK ROADResaleStrata96.0013646NaNCondominium1Freehold21Rest of Central Region06 to 10202122021Q1
42003URBAN TREASURES1234700635.0818472021-06-01JALAN EUNOSNew SaleStrata59.00198811,172,965.00Condominium1Freehold14Outside Central Region06 to 10202162021Q2
272THE CLEMENTVALE19500001,614.6012082021-04-01MAS KUNING TERRACEResaleLand150.0013000NaNTerrace House199 yrs lease commencing from 19975Outside Central Region-202142021Q2
127143OLEANAS RESIDENCE32000001,668.4219182023-10-01KIM YAM ROADResaleStrata155.0020645NaNCondominium1Freehold9Core Central Region21 to 252023102023Q4
25272THE ORIE25870001,044.1124782025-01-01LORONG 1 TOA PAYOHNew SaleStrata97.0026670NaNCondominium199 yrs lease commencing from 202412Rest of Central Region01 to 05202512025Q1
76513SENGKANG GRAND RESIDENCES1570800936.4716772020-11-01COMPASSVALE BOWNew SaleStrata87.0018055NaNApartment199 yrs lease commencing from 201819Outside Central Region06 to 102020112020Q4

Duplicate rows

Most frequently occurring

Project NameTransacted Price ($)Area (SQFT)Unit Price ($ PSF)Sale DateStreet NameType of SaleType of AreaArea (SQM)Unit Price ($ PSM)Nett Price($)Property TypeNumber of UnitsTenurePostal DistrictMarket SegmentFloor LevelSale YearSale Month# duplicates
0NORMANTON PARK899797516.6717422021-01-01NORMANTON PARKNew SaleStrata48.0018746NaNApartment199 yrs lease commencing from 20195Rest of Central Region11 to 15202112
1PARKTOWN RESIDENCE25060001,065.6423522025-02-01TAMPINES STREET 62New SaleStrata99.0025313NaNApartment199 yrs lease commencing from 202318Outside Central Region11 to 15202522
2THE ORIE1781000645.8427582025-01-01LORONG 1 TOA PAYOHNew SaleStrata60.0029683NaNCondominium199 yrs lease commencing from 202412Rest of Central Region11 to 15202512